{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1aa4bccf3a7fd6db",
   "metadata": {},
   "source": [
    "# 适配器"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b61415a88c13ee",
   "metadata": {},
   "source": [
    "Cqlib 通过 `cqlib-adapter` 适配器库实现与主流量子计算生态的兼容与集成：\n",
    "- 当前支持：Qiskit 扩展模块\n",
    "- 开发计划：将逐步集成 Pennylane 等主流量子计算框架"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbaf246225a89f7d",
   "metadata": {},
   "source": [
    "## 1. 安装 Cqlib-Adapter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95d531972b3f5cb",
   "metadata": {},
   "source": [
    "推荐使用 pip 包管理器安装适配器库： \n",
    "\n",
    "```shell\n",
    "pip install cqlib-adapter\n",
    "```\n",
    "\n",
    "提示：安装前请确保已配置 Python 环境（3.10及以上），并将 pip 升级至最新版本。\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9625e287edf661",
   "metadata": {},
   "source": [
    "## 2. Qiskit 扩展模块"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ec0d1cd33ff595",
   "metadata": {},
   "source": [
    "主要功能：\n",
    "\n",
    "- 支持 QCIS 量子门：兼容天衍平台的原生量子门集，比如 `X2P`,`X2M`,`Y2P`,`Y2M`,`XY2P`,`XY2M`；\n",
    "- 后端设备集成 ：可无缝对接天衍量子云平台进行任务执行；\n",
    "- 自动编译优化 ：可智能转换与优化量子线路以适配物理设备执行需求。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1305d9554262ac",
   "metadata": {},
   "source": [
    "### 2.1 QCIS 量子门"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5240b37fb6cba0",
   "metadata": {},
   "source": [
    "详细说明请参考：[QCIS 指令说明](https://qc.zdxlz.com/learn/#/resource/informationSpace?lang=zh&cId=/mkdocs/zh/appendix/QCIS_instruction_set.html)\n",
    "\n",
    "以下为 Qiskit 扩展中支持的 QCIS 原生命令说明：\n",
    "- `X2P`: X轴方向旋转 π/2\n",
    "- `X2M`: X轴方向旋转 -π/2\n",
    "- `Y2P`: Y轴方向旋转 π/2\n",
    "- `Y2M`: Y轴方向旋转 -π/2\n",
    "- `XY2P`: XY轴方向旋转一个指定角度\n",
    "- `XY2M`: XY轴方向旋转一个指定角度\n",
    "\n",
    "\n",
    "> 特别说明：尽管 H 门并非天衍物理机的原生指令，但为提升编译效率与兼容性，扩展模块保留了 H 门的表达。在实际运行时，系统将自动将 H 门分解为 Y2P 和 RZ 的组合门操作。      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2259c8b4711ca9",
   "metadata": {},
   "source": [
    "### 2.2 使用示例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144a6ce8b2134fd2",
   "metadata": {},
   "source": [
    "#### 2.2.1 构建量子电路"
   ]
  },
  {
   "cell_type": "code",
   "id": "9768e3c094a7a2ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:08.551932Z",
     "start_time": "2025-06-10T02:59:05.435159Z"
    }
   },
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from cqlib_adapter.qiskit_ext import X2PGate, X2MGate\n",
    "\n",
    "# 创建量子寄存器与经典寄存器\n",
    "qs = QuantumRegister(2)\n",
    "cs = ClassicalRegister(2)\n",
    "circuit = QuantumCircuit(qs, cs)\n",
    "\n",
    "# 构建量子线路\n",
    "circuit.x(qs[1])           # Pauli-X 门\n",
    "circuit.h(qs[0])           # Hadamard 门（将自动分解为物理门）\n",
    "circuit.cx(qs[0], qs[1])   # CNOT 门\n",
    "circuit.append(X2PGate(), [qs[0]])  # 添加 X2P 门\n",
    "circuit.append(X2MGate(), [qs[1]])  # 添加 X2M 门\n",
    "circuit.barrier(qs)\n",
    "circuit.measure(qs, cs)    #测量操作\n",
    "\n",
    "circuit.draw()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      ┌───┐     ┌─────┐ ░ ┌─┐   \n",
       "q0_0: ┤ H ├──■──┤ X2p ├─░─┤M├───\n",
       "      ├───┤┌─┴─┐├─────┤ ░ └╥┘┌─┐\n",
       "q0_1: ┤ X ├┤ X ├┤ X2m ├─░──╫─┤M├\n",
       "      └───┘└───┘└─────┘ ░  ║ └╥┘\n",
       "c0: 2/═════════════════════╩══╩═\n",
       "                           0  1 "
      ],
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">      ┌───┐     ┌─────┐ ░ ┌─┐   \n",
       "q0_0: ┤ H ├──■──┤ X2p ├─░─┤M├───\n",
       "      ├───┤┌─┴─┐├─────┤ ░ └╥┘┌─┐\n",
       "q0_1: ┤ X ├┤ X ├┤ X2m ├─░──╫─┤M├\n",
       "      └───┘└───┘└─────┘ ░  ║ └╥┘\n",
       "c0: 2/═════════════════════╩══╩═\n",
       "                           0  1 </pre>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "a610c1b43a8f6574",
   "metadata": {},
   "source": [
    "#### 2.2.2 后端设备接入"
   ]
  },
  {
   "cell_type": "code",
   "id": "f45f8a6665a48f02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:30.581693Z",
     "start_time": "2025-06-10T02:59:29.670532Z"
    }
   },
   "source": [
    "from cqlib_adapter.qiskit_ext import TianYanProvider\n",
    "\n",
    "# 初始化认证\n",
    "provider = TianYanProvider(token='<your-token>')\n",
    "# 获取所有可用的后端设备\n",
    "backends = provider.backends()\n",
    "backends"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<TianYanQuantumBackend('tianyan176')>,\n",
       " <TianYanQuantumBackend('tianyan176-2')>,\n",
       " <TianYanQuantumBackend('tianyan24')>,\n",
       " <TianYanQuantumBackend('tianyan504')>,\n",
       " <TianYanSimulatorBackend('tianyan_sw')>,\n",
       " <TianYanSimulatorBackend('tianyan_s')>,\n",
       " <TianYanSimulatorBackend('tianyan_tn')>,\n",
       " <TianYanSimulatorBackend('tianyan_tnn')>,\n",
       " <TianYanSimulatorBackend('tianyan_sa')>,\n",
       " <TianYanSimulatorBackend('tianyan_swn')>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "05202d9f-f4e9-48cc-95b6-a15e348332e0",
   "metadata": {},
   "source": [
    "获取指定设备："
   ]
  },
  {
   "cell_type": "code",
   "id": "1dfd3880-6aa0-4f3b-9c72-a33643a40635",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:37.227468Z",
     "start_time": "2025-06-10T02:59:36.893252Z"
    }
   },
   "source": [
    "# 获取指定后端设备 (如'tianyan176-2')\n",
    "backend = provider.backend('tianyan176-2')\n",
    "backend"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TianYanQuantumBackend('tianyan176-2')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "cf207a4d8f1e8fb7",
   "metadata": {},
   "source": [
    "#### 2.2.3 拓扑适配"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebd2af7",
   "metadata": {},
   "source": [
    "系统会自动根据后端设备的拓扑结构调整量子线路布局，用户无需手动处理物理比特映射。"
   ]
  },
  {
   "cell_type": "code",
   "id": "b185a8f4b8933da4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:40.264129Z",
     "start_time": "2025-06-10T02:59:39.997618Z"
    }
   },
   "source": [
    "from qiskit import transpile\n",
    "\n",
    "tqc = transpile(circuit, backend=backend)\n",
    "tqc.draw(idle_wires=False)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "global phase: π/2\n",
       "           ┌─────┐┌─────┐┌───┐    ┌───┐ ┌─────┐ ░    ┌─┐\n",
       "q0_1 -> 51 ┤ X2p ├┤ X2p ├┤ H ├─■──┤ H ├─┤ X2m ├─░────┤M├\n",
       "           └┬───┬┘└─────┘└───┘ │ ┌┴───┴┐└─────┘ ░ ┌─┐└╥┘\n",
       "q0_0 -> 57 ─┤ H ├──────────────■─┤ X2p ├────────░─┤M├─╫─\n",
       "            └───┘                └─────┘        ░ └╥┘ ║ \n",
       "     c0: 2/════════════════════════════════════════╩══╩═\n",
       "                                                   0  1 "
      ],
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">global phase: π/2\n",
       "           ┌─────┐┌─────┐┌───┐    ┌───┐ ┌─────┐ ░    ┌─┐\n",
       "q0_1 -> 51 ┤ X2p ├┤ X2p ├┤ H ├─■──┤ H ├─┤ X2m ├─░────┤M├\n",
       "           └┬───┬┘└─────┘└───┘ │ ┌┴───┴┐└─────┘ ░ ┌─┐└╥┘\n",
       "q0_0 -> 57 ─┤ H ├──────────────■─┤ X2p ├────────░─┤M├─╫─\n",
       "            └───┘                └─────┘        ░ └╥┘ ║ \n",
       "     c0: 2/════════════════════════════════════════╩══╩═\n",
       "                                                   0  1 </pre>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "7be5b340-94c4-4aa7-a643-6638da701cdb",
   "metadata": {},
   "source": [
    "#### 2.2.4 基于 `Backend` 运行任务"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58720df103c2d99f",
   "metadata": {},
   "source": [
    "`backend.run` 接口可自动适配目标设备的拓扑结构，用户无需手动调整比特映射或线路结构。"
   ]
  },
  {
   "cell_type": "code",
   "id": "aee77df9-6133-4104-bffe-20a1e308c29e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T03:00:17.176971Z",
     "start_time": "2025-06-10T03:00:13.260158Z"
    }
   },
   "source": [
    "backend = provider.backend('tianyan_sw')\n",
    "\n",
    "# 在指定后端运行量子线路\n",
    "job = backend.run([circuit], shots=3000)\n",
    "\n",
    "# 获取并输出任务结果\n",
    "print(f'Job ID: {job.job_id()}')\n",
    "print(f'Job Result: {job.result().get_counts()}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: 1932271557272739841\n",
      "Job Result: {'10': 1502, '1': 1498}\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "1fe35e88-e27a-46ab-997b-7537d5de9e61",
   "metadata": {},
   "source": [
    "#### 2.2.5 基于 `Sampler` 运行任务"
   ]
  },
  {
   "cell_type": "code",
   "id": "995cde0f-6729-4883-a55d-d43b2f10b62b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T03:00:40.391298Z",
     "start_time": "2025-06-10T03:00:28.200484Z"
    }
   },
   "source": [
    "from cqlib_adapter.qiskit_ext import TianYanSampler\n",
    "\n",
    "# 初始化采样器，绑定后端设备\n",
    "sampler = TianYanSampler(backend=backend)\n",
    "\n",
    "# 提交量子线路并运行\n",
    "job = sampler.run([circuit], shots=3000)\n",
    "\n",
    "# 获取并输出任务结果\n",
    "print(f'Job ID: {job.job_id()}')\n",
    "print(f'Job Result: {job.result()}')\n",
    "\n",
    "# 默认经典寄存器命名为 c0\n",
    "# cs = ClassicalRegister(2)\n",
    "print(f'Counts: {job.result()[0].data.c0.get_counts()}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: 05547ce8-6f48-4d94-a39c-3aa5d5c8c771\n",
      "Job Result: PrimitiveResult([SamplerPubResult(data=DataBin(c0=BitArray(<shape=(), num_shots=3000, num_bits=2>)), metadata={'shots': 3000, 'circuit_metadata': {}})], metadata={'version': 2})\n",
      "Counts: {'10': 1468, '01': 1532}\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "85bedb96-f7d2-4342-aa28-5e5a08cd5394",
   "metadata": {},
   "source": [
    "### 2.3 Qiskit 生态兼容"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15a75da-2ccc-4eeb-8164-18a699e6d72f",
   "metadata": {},
   "source": [
    "通过 Cqlib-adapter， 用户可以在 Qiskit 生态系统中（如 qiskit-machine-learning）无缝使用天衍量子计算云平台的算力资源。以下以建构一个简单的 [神经网络分类器应用](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/02_neural_network_classifier_and_regressor.html) 为示例，演示如何调用天衍后端完成训练任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ea16c9-11be-4461-bc7b-1945bf76e78d",
   "metadata": {},
   "source": [
    "#### 2.3.1 数据准备与可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "795352bb-5309-407a-94fe-02bebfe6766b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:11.583605400Z",
     "start_time": "2025-05-20T08:43:39.588954Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "\n",
    "from qiskit.circuit.library import RealAmplitudes\n",
    "from qiskit_machine_learning.optimizers import COBYLA\n",
    "from qiskit_machine_learning.utils import algorithm_globals\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier\n",
    "from qiskit_machine_learning.neural_networks import SamplerQNN\n",
    "from qiskit_machine_learning.circuit.library import QNNCircuit\n",
    "\n",
    "algorithm_globals.random_seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "716e4edc-104b-44f9-a7a2-3dbd62ea4a4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:11.586621400Z",
     "start_time": "2025-05-20T08:43:42.250768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成二分类数据集\n",
    "num_inputs = 2\n",
    "num_samples = 20\n",
    "X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1\n",
    "\n",
    "# 标签转换：y ∈ {0,1} → y ∈ {-1,+1}\n",
    "y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}\n",
    "y = 2 * y01 - 1  # in {-1, +1}\n",
    "y_one_hot = np.zeros((num_samples, 2))\n",
    "for i in range(num_samples):\n",
    "    y_one_hot[i, y01[i]] = 1\n",
    "\n",
    "# 可视化数据分布\n",
    "for x, y_target in zip(X, y):\n",
    "    if y_target == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bdfb259-8b21-4a31-be80-08ec628b9005",
   "metadata": {},
   "source": [
    "#### 2.3.2 量子神经网络构建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284bd055",
   "metadata": {},
   "source": [
    "构建量子特征映射和可训练线路，集成 Qiskit 神经网络模块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8531c758-8a40-4e86-bdd9-18f47b0c6d3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:11.612966Z",
     "start_time": "2025-05-20T08:43:42.499447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌──────────────────────────┐┌──────────────────────────────────────┐\n",
       "q_0: ┤0                         ├┤0                                     ├\n",
       "     │  ZZFeatureMap(x[0],x[1]) ││  RealAmplitudes(θ[0],θ[1],θ[2],θ[3]) │\n",
       "q_1: ┤1                         ├┤1                                     ├\n",
       "     └──────────────────────────┘└──────────────────────────────────────┘</pre>"
      ],
      "text/plain": [
       "     ┌──────────────────────────┐┌──────────────────────────────────────┐\n",
       "q_0: ┤0                         ├┤0                                     ├\n",
       "     │  ZZFeatureMap(x[0],x[1]) ││  RealAmplitudes(θ[0],θ[1],θ[2],θ[3]) │\n",
       "q_1: ┤1                         ├┤1                                     ├\n",
       "     └──────────────────────────┘└──────────────────────────────────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QNNCircuit(ansatz=RealAmplitudes(num_inputs, reps=1))\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b4c696-b867-4197-b040-e91badc1a67c",
   "metadata": {},
   "source": [
    "#### 2.3.3 指定后端设备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aecc40c5-e941-40ad-96e6-6562578d2fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cqlib_adapter.qiskit_ext import TianYanProvider, TianYanSampler\n",
    "\n",
    "# 初始化并指定后端量子设备\n",
    "provider = TianYanProvider(token='<your-token>')\n",
    "sampler = TianYanSampler(provider.backend('tianyan_sw'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a38336-85b3-4c6d-ba12-467ab291328d",
   "metadata": {},
   "source": [
    "#### 2.3.4 模型训练与优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5087329-59a2-4b33-988b-90ced277aa35",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:11.625103400Z",
     "start_time": "2025-05-20T08:43:42.734358Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "from qiskit.primitives import StatevectorSampler as Sampler\n",
    "\n",
    "# 定义奇偶函数，将比特串映射为 0 或 1\n",
    "def parity(x):\n",
    "    return \"{:b}\".format(x).count(\"1\") % 2\n",
    "\n",
    "output_shape = 2  # 对应分类任务的输出维度（即奇偶映射的两种可能结果）\n",
    "\n",
    "# 构建量子神经网络 SamplerQNN\n",
    "sampler_qnn = SamplerQNN(\n",
    "    circuit=qc,\n",
    "    interpret=parity,\n",
    "    output_shape=output_shape,\n",
    "    sampler=sampler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3506aa5a-c8ef-406b-9f99-b73344a5d26a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:59:11.627103600Z",
     "start_time": "2025-05-20T08:43:58.409922Z"
    }
   },
   "outputs": [],
   "source": [
    "# 回调函数：在调用 .fit() 时动态绘制目标函数值变化图\n",
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 配置分类器，设置优化器和回调函数\n",
    "sampler_classifier = NeuralNetworkClassifier(\n",
    "    neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1981f91-552d-4fcf-9301-93bf08e9b6e6",
   "metadata": {},
   "source": [
    "#### 2.3.5 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6653309e-8fef-4ed6-900f-198281a37e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化用于回调函数的目标函数值记录数组\n",
    "objective_func_vals = []\n",
    "\n",
    "# 设置绘图尺寸\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# 拟合分类器\n",
    "sampler_classifier.fit(X, y01)\n",
    "\n",
    "# 恢复默认绘图尺寸\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# 评估分类器性能\n",
    "sampler_classifier.score(X, y01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f740c7d-853a-4cbf-a98d-05a530731363",
   "metadata": {},
   "source": [
    "#### 2.3.6 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97b7178-15ea-4aa2-8bd3-dde3104856fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 执行模型预测\n",
    "y_predict = sampler_classifier.predict(X)\n",
    "\n",
    "# 结果可视化：红圈标出分类错误的样本\n",
    "for x, y_target, y_p in zip(X, y01, y_predict):\n",
    "    if y_target == 1:\n",
    "        plt.plot(x[0], x[1], \"bo\")\n",
    "    else:\n",
    "        plt.plot(x[0], x[1], \"go\")\n",
    "    if y_target != y_p:\n",
    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
    "\n",
    "# 绘制参考分界线\n",
    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
